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AI is increasingly playing a pivotal role in businesses and organizations,impacting the outcomes and interests of human users. Automated Machine Learning(AutoML) streamlines the machine learning model development process byautomating repetitive tasks and making data-driven decisions, enabling evennon-experts to construct high-quality models efficiently. This democratizationallows more users (including non-experts) to access and utilizestate-of-the-art machine-learning expertise. However, AutoML tools may alsopropagate bias in the way these tools handle the data, model choices, andoptimization approaches adopted. We conducted an experimental study ofUser-interface-based open source AutoML tools (DataRobot, H2O Studio, Dataiku,and Rapidminer Studio) to examine if they had features to assist users indeveloping fairness-aware machine learning models. The experiments covered thefollowing considerations for the evaluation of features: understanding use casecontext, data representation, feature relevance and sensitivity, data bias andpreprocessing techniques, data handling capabilities, training-testing split,hyperparameter handling, and constraints, fairness-oriented model development,explainability and ability to download and edit models by the user. The resultsrevealed inadequacies in features that could support in fairness-aware modeldevelopment. Further, the results also highlight the need to establish certainessential features for promoting fairness in AutoML tools.

Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools